White Paper

Pushing the Reliability Envelope: Digital Optimization for the “Always On” Refinery

AspenTech conducted a survey of 240 downstream customers to uncover thoughts and opinions on digital optimization and industry trends for 2018 and beyond. This white paper details the results of the survey and provides the reader with insights on the focus of increased reliability, a top priority for many refinery organizations.

White Paper

低接触式机器学习助力实现资产绩效管理

单独的传统预防型维护无法解决非预期停机问题。凭借低接触式机器学习所驱动的资产绩效管理,现在可能会从数十种程序、资产和维护数据中抽取相关数值,从而优化资产绩效。在本白皮书中,将学习这种插断性技术如何部署精确性故障模式识别,其具有较高的准确性,可以提前预测设备停机月数,并就约定的维护提供相关建议。本白皮书亦列述了驱动先进可靠性管理的五个最佳实践,以期增产提盈。

White Paper

資料:手軽な機械学習が資産パフォーマンス管理(APM)の可能性を開く(日本語)

従来の対処的メンテナンスだけでは、不測の事態に対応できません。手軽な機械学習による資産パフォーマンス管理(APM) により、今や製造工場のスタッフが、何十年にもわたって蓄積してきた設計や運用データから容易に価値を引き出し、資産(主に装置などのハードウェア)のパフォーマンスをより適切に管理して最適化することが可能になりました。本書では、常識を覆すような画期的なテクノロジーがどのように精密な故障パターン認識を使い、高精度に装置の故障を数ヶ月も前に予測し、処方的なメンテナンスをガイダンスするかを説明しています。また、5つのベストプラクティス(運用方法)をご紹介し、最先端の信頼性管理による、さらなる生産効率および利益率の改善手法について述べています。

White Paper

Low-Touch Machine Learning is Fulfilling the Promise of Asset Performance Management

Traditional preventive maintenance alone cannot solve the problems of unexpected breakdowns. With asset performance management powered by low-touch machine learning, it’s now possible to extract value from decades of process, asset and maintenance data to optimize asset performance. In this white paper, learn how this disruptive technology deploys precise failure pattern recognition with very high accuracy to predict equipment breakdowns months in advance and advise on prescriptive maintenance. The paper also outlines five best practices for driving state-of-the-art reliability management to increase production and profitability.

White Paper

Low-Touch Machine Learning is Fulfilling the Promise of Asset Performance Management

Traditional preventive maintenance alone cannot solve the problems of unexpected breakdowns. With asset performance management powered by low-touch machine learning, it’s now possible to extract value from decades of process, asset and maintenance data to optimize asset performance. In this white paper, learn how this disruptive technology deploys precise failure pattern recognition with very high accuracy to predict equipment breakdowns months in advance and advise on prescriptive maintenance. The paper also outlines five best practices for driving state-of-the-art reliability management to increase production and profitability.

White Paper

Low-Touch Machine Learning is Fulfilling the Promise of Asset Performance Management

Traditional preventive maintenance alone cannot solve the problems of unexpected breakdowns. With asset performance management powered by low-touch machine learning, it’s now possible to extract value from decades of process, asset and maintenance data to optimize asset performance. This white paper describes five best practices for driving state-of-the-art reliability management to predict breakdowns months in advance—increasing production and profitability.

Aspen Mtell

AspenTech's Aspen Mtell maintenance software stops machines from failing, increases performance, reduces maintenance costs, and increases production.

Aspen ProMV

AspenTech ProMV provides multivariate analysis to interpret what’s driving the variability among the thousands of variables in your processes manufacturing.

White Paper

Solving Three Common Problems Through SRU Simulation

Small operational issues in the sulphur recovery unit (SRU) can lead to higher emisisons — or worse, a frustrating and costly shutdown. Simulation of the SRU can enable more reliable operations and fewer shutdowns through the prevention of issues and quick and effective troubleshooting. In this paper, we present three common operational issues and show how simulation was used to prevent or quickly resolve the situation.

White Paper

BPCL Mumbai Refinery Enhances Energy Management Using AspenTech Solutions

Energy is often the largest operating expense after raw materials for refining and petrochemicals companies, frequently starting out at over 50 percent of operating costs prior to energy reduction programs. Managing and optimizing these energy costs are critical capabilities for a refinery to meet profitability and sustainability targets. This white paper details the journey Bharat Petroleum Mumbai Refinery (BPCL-MR) in Mahul, India underwent to improve operations while optimizing energy costs.

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